Contextualization for historians in industrial systems

ABSTRACT

Systems and methods that discover relations and correlates among disparate pieces of data, to infer possible relationships between the industrial process and historian data/events to improve industrial operations. A correlation component can employ heuristic models to capture process data/event data, and can further include an implicit correlation component and an explicit correlation component. Accordingly, relations among various parameters can be discovered (e.g., dynamically) and proper corrective adjustments supplied to the industrial process.

TECHNICAL FIELD

The subject innovation relates generally to historians associated withindustrial controllers and more particularly to historians that inferrelationships between disparate events (e.g., non-time series events)and operation of the industrial process (e.g., an outcome of theprocess).

BACKGROUND

Industrial controllers are special-purpose computers utilized forcontrolling industrial processes, manufacturing equipment, and otherfactory automation, such as data collection or networked systems. At thecore of the industrial control system, is a logic processor such as aProgrammable Logic Controller (PLC) or PC-based controller. ProgrammableLogic Controllers for instance, are programmed by systems designers tooperate manufacturing processes via user-designed logic programs or userprograms. The user programs are stored in memory and generally executedby the PLC in a sequential manner although instruction jumping, loopingand interrupt routines, for example, are also common. Associated withthe user program are a plurality of memory elements or variables thatprovide dynamics to PLC operations and programs. Differences in PLCs aretypically dependent on the number of Input/Output (I/O) they canprocess, amount of memory, number and type of instructions, and speed ofthe PLC central processing unit (CPU).

In a more macro sense than the controller, businesses have become morecomplex in that higher order business systems or computers often need toexchange data with such controllers. For instance, an industrialautomation enterprise may include several plants in different locations.Modern drivers such as efficiency and productivity improvement, andcost-reduction, are requiring manufacturers to collect, analyze, andoptimize data and metrics from global manufacturing sites. For example,a food company can have several plants located across the globe forproducing a certain brand of food. These factories in the past werestandalone, with minimum data collection and comparison of metrics withother similar factories. In the networked world of today, manufacturersare demanding real-time data from their factories to drive optimizationand productivity. Unfortunately, conventional control systemsarchitectures are not equipped to allow a seamless exchange of databetween these various components of the enterprise.

Another requirement of modern control system architectures is theability to record and store data in order to maintain compliance withadministrative regulations. One common solution for recording dataincludes providing a local recording module that often occupies a slotin a controller backplane such as a PC-Historian which is an industrialcomputer for the controller backplane, and employs a transitional layerto supply an indirect interface to the controller. This includes aplatform that provides high speed, time series, data storage andretrieval with both local and remote control processors. ThePC-Historian communicates with controllers directly through thebackplane and can communicate remotely via a network interface. ThePC-Historian allows archiving data from the controller to an ArchiveEngine which provides additional storage capabilities.

Moreover, control modules can be spatially distributed along a commoncommunication link in several locations, wherein such controllers canthen communicate with each other, and/or with historians or applicationsoutside of a control environment (e.g., data collection systems/businessrelated systems and applications). Accordingly, information management,such as message exchanges that employ different protocols andconfigurations are becoming complex. For example, the mapping ofinformation from production management to process control and customglue code for integrating systems with different protocols and formatscan create configuration and management difficulties.

Furthermore, failed communications (e.g., messages that are not receivedor acted upon), delayed responses (e.g., as a function of the timedifference between a sent message and a re-send), and additionaloverhead (e.g., consumption of processing cycles to review storednotifications, schedule re-transmissions and re-send messages) furtheradd to the problems involved.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects described herein. This summary is not anextensive overview nor is intended to identify key/critical elements orto delineate the scope of the various aspects described herein. Its solepurpose is to present some concepts in a simplified form as a prelude tothe more detailed description that is presented later.

The subject innovation provides for a historian(s) with a correlationcomponent(s) that discovers relations and correlates among disparatepieces of data, to infer possible relationships between historiandata/events and the industrial process (e.g., predict an outcomethereof). The correlation component can employ a heuristic model tocapture process data/event data, and further include an implicitcorrelation component and an explicit correlation component. Theexplicit correlation component can employ predetermined models that areset by a user/external data sources, and the implicit correlationcomponent can deduce relations among causes of triggering events (e.g.,dynamically and/or in real-time during operation). For example, insteadof merely storing values, messages that cause transition of values canbe stored and compared via the implicit correlation component to derivecorrelations among various states that share the same messages.Accordingly, relations among various parameters can be discovered (e.g.,dynamically) and proper corrective adjustments supplied to theindustrial process.

In accordance to a related methodology of the subject innovation,initially a set of data related to the industrial process can becollected. Such data can then be correlated to a predetermined model anda model that best fits (e.g., statistically) can subsequently beselected. Accordingly, quality analysis can occur ahead of processingand during the control process via employing historian data at variousgranularity levels. Such granularity levels of datacollection/implementation can depend upon factors such as: the nature ofthe manufacturing process; outcome of the quality control tests;criticality of operation, and the like. Moreover, based on suchhistorian data, the quality control process of the subject innovationcan predict outcome of quality for the industrial process, and initiatecorrection actions in view of current values of data. For example,threshold values can be set to determine and trigger various actionsduring execution, such as: automatically performing corrective measuresand maintenance procedures; invoking embedded files within a processthat can be utilized by other components or reviewed by an operator;providing Just-In-Time training to an operator during various stages ofthe process; spawning other automatic procedures during various stagesof industrial production, and the like. By associating historians withquality procedures, timely, tighter and more stringent controls can beapplied to various automation processes—thus increasing overall qualityin an automated manufacturing environment.

To the accomplishment of the foregoing and related ends, certainillustrative aspects are described herein in connection with thefollowing description and the annexed drawings. These aspects areindicative of various ways which can be practiced, all of which areintended to be covered herein. Other advantages and novel features maybecome apparent from the following detailed description when consideredin conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of a correlation component thatcorrelates among disparate pieces of data in accordance with an aspectof the subject innovation.

FIG. 2 illustrates a particular correlation component that discovers andidentifies relationships among the events/data aspect for a correlationwith the industrial processes in accordance with an aspect of thesubject innovation.

FIG. 3 illustrates a general block diagram of a correlation componentthat can dynamically infer relationships according to an aspect of thesubject innovation.

FIG. 4 illustrates a related methodology of inferring outcome ofindustrial process in accordance with an aspect of the subjectinnovation.

FIG. 5 illustrates a related methodology of relationship deduction inaccordance with an aspect of the subject innovation.

FIG. 6 illustrates a correlation component that interacts with a machinelearning system that has an inference component, in accordance with anaspect of the subject innovation.

FIG. 7 illustrates a system that includes a plurality of embeddedhistorian components operating in an organizational data model, whereina correlation component infers various trends.

FIG. 8 illustrates a correlation component that is operatively connectedto embedded historians in accordance with an aspect of the subjectinnovation.

FIG. 9 illustrates an exemplary industrial automation network that canimplement a correlation component in accordance with an aspect of thesubject innovation.

FIG. 10 illustrates an exemplary multi-tiered and distributed historiansystem, in accordance with an aspect of the subject innovation.

FIG. 11 illustrates historian services that include historian dataservices and presentation and reporting services.

FIG. 12 illustrates an exemplary environment that can employ acorrelation component for inference of trends in accordance with anaspect of the subject innovation.

DETAILED DESCRIPTION

The various aspects of the subject innovation are now described withreference to the annexed drawings, wherein like numerals refer to likeor corresponding elements throughout. It should be understood, however,that the drawings and detailed description relating thereto are notintended to limit the claimed subject matter to the particular formdisclosed. Rather, the intention is to cover all modifications,equivalents and alternatives falling within the spirit and scope of theclaimed subject matter.

FIG. 1 illustrates a correlation component 110 that is associated with aplant embedded historian network of an industrial programmer system 100(e.g., a network of controller devices), which facilitates correlationamong disparate pieces of data/event 111, 112, 113, to improveindustrial operations. Such data/events can include historian data,activity, alarms, commands, messages, and the like, which areestablished for the industrial setting to indicate operation trends. Forexample, an alarm can include a specialization of conditionevent—wherein the initiation of such alarm can occur via an alarm event,and a message that carries the alarm to the receiver can signify analarm message. Likewise, an activity can occur at a given point in timein the system, wherein an automation product generates and receivesevents.

The information associated with an event can be transmitted as a messagebetween source and receiver of event information, for example. Thecorrelation component 110 can supply a correlation between thedata/events, to infer an outcome of the industrial process, for example.Moreover, relations among various parameters and data/events 111, 112,113 can be discovered to determine and/or predict an outcome of anindustrial process on-the-fly. Such correlation component 110 canfurther enable a tight control and short reaction time to processparameters, and for a modification thereof. Moreover, issues related totime lags associated with conventional factory controllers can bemitigated, as process parameter can be readily discovered and adapted.

The correlation component 110 can discover relations from data collectedby embedded historians 121, 122, 123 (1 thru m, m being an integer) thatare distributed on the back plane of an industrial network. Data can becollected via such embedded historians in accordance with anorganizational model of a hierarchical system that is distributed acrossvarious elements of an enterprise, for example. In contrast toconventional PC historians, embedded historians (e.g., micro historians)of the subject innovation are special purpose historians that reside ina backplane and supply direct interface (e.g., without a transitionlayer) to controllers and/or associated industrial units. Such embeddedhistorians employ industrial specifications (e.g., regarding shockvibration, sealing, contamination proofing, and the like), and supplysubstantially higher data exchange speed as compared to conventional PChistorians.

FIG. 2 illustrates a particular correlation component that discovers andidentifies relationships among the events/data aspect for a correlationwith the industrial processes in accordance with an aspect of thesubject innovation. The correlation component 210 can employ a heuristicmodel to capture process data/event data, and further include animplicit correlation component 220 and an explicit correlation component230. The explicit correlation component 230 can employ predeterminedmodels that are set by a user/external data sources and saved within astorage medium 212, and the implicit correlation component 220 candeduce relations among causes of triggering events. For example, insteadof merely storing values, messages that caused transition of values canbe stored and compared via the implicit correlation component 220 toderive correlations among various states that share the same messages.Accordingly, relations among various parameters can be discovered (e.g.,dynamically) and proper corrective adjustments supplied to theindustrial process 215.

FIG. 3 illustrates a general block diagram of a correlation component315 that can dynamically infer relationships between disparate pieces ofdata and operation of the industrial unit 300. The correlation component315 can interact with a quality analysis system that can perform qualityanalysis. Such quality analysis can occur ahead of processing and/orduring the control process via employing historian data—at variousgranularity levels, which can depend upon factors such as: the nature ofthe manufacturing process; outcome of the quality control tests; and thelike. Moreover, based on such historian data that is collected via theembedded historians 335, 337, and 339 (1 to k, k being an integer), thequality control process of the subject innovation can predict outcome ofquality for the industrial process—and further initiate correctionprocess in view of current value of data. For example, threshold valuescan be set to determine and trigger various actions during execution,such as: automatically performing corrective measures and maintenanceprocedures; invoking embedded files within a process that can beutilized by other components or reviewed by an operator; providingJust-In-Time training to an operator during various stages of theprocess; and/or spawning other automatic procedures during variousstages of industrial production. By associating historians to qualityprocedures, timely, tighter and more stringent controls can be appliedto various automation processes—thus increasing overall quality in anautomated manufacturing environment. Data can be initially stored viahistorians 335, 337, and 339, and such storage can continue untilpredetermined threshold storage capacities associated with thesehistorians are reached. Upon reaching such predetermined threshold, thestored data (e.g., history data) can be evaluated and the embeddedhistorians 335, 337, and 339 notified to indicate that the data is nolonger required and/or is not necessary for future access and hence canbe overwritten.

FIG. 4 illustrates a methodology 400 for prediction of outcomes based onmatching of events with predetermined models in accordance with anaspect of the subject innovation. While the exemplary method isillustrated and described herein as a series of blocks representative ofvarious events and/or acts, the present invention is not limited by theillustrated ordering of such blocks. For instance, some acts or eventsmay occur in different orders and/or concurrently with other acts orevents, apart from the ordering illustrated herein, in accordance withthe invention. In addition, not all illustrated blocks, events or acts,may be required to implement a methodology in accordance with thepresent invention. Moreover, it will be appreciated that the exemplarymethod and other methods according to the invention may be implementedin association with the method illustrated and described herein, as wellas in association with other systems and apparatus not illustrated ordescribed. Initially and at 410 a set of data/events related to theindustrial process can be collected. Next and at 420 such collected datacan be compared to predetermined patterns, and a matching patternsubsequently selected (e.g., via a plurality of statistical models) at430. An outcome of the industrial process can then be inferred at 440.

FIG. 5 illustrates a related methodology 500 of supplying correlationamong disparate events to deduce a trend in accordance with an aspect ofthe subject innovation. Initially and at 510, an industrial plant thatemploys a plurality of embedded historians is activated and comeson-line. At 520, such embedded historians can be configured according toa predetermined setting. For example, tags in an embedded historian canbe automatically created, and be set up as a default collection for aplant scan, such that when a plant comes on-line, the embeddedhistorians announce their presence to such plant, and are discoveredthereby. Moreover, the configuration of the embedded historians caninclude, editing process variables, automation device names, creatingtag references, data models, hierarchy, simulation of industrialprocesses, and the like. Based on such configuration, embeddedhistorians can subsequently collect data related to the industrialprocess at 530. At 540 a determination is made regarding existence oftrends among such collected data and discovery of content that isrelated to each other. Hence, algorithmically-deduced relationshipsbetween events and outcomes of an industrial process can be established,wherein in addition to adhering to predefined set of hierarchalcategories, the subject innovation enables discovery of relations amongindividual/collective user(s). By leveraging the relationships that isdeduced (e.g., statistical relationships that exist in unique ways, thesubject innovation can discover content that is related to each other,such as between events and outcome of the process.

FIG. 6 illustrates a correlation component 611 that interacts with amachine learning system 600 that has an inference component 610, inaccordance with an aspect of the subject innovation. The system 600infers relationship between events/data collected and outcome of anindustrial process. Thus, in addition to adhering to a predefined set ofmodels for prediction of outcomes for processes (e.g., explicitcorrelation), the system 600 allows inferring dynamic models duringoperation of the industrial process and in real-time. As explainedearlier, by leveraging the relationships that exist between operatingparameters involved (e.g., temperature, crew members involved in theindustrial line, time, and the like), the correlation component candiscover content that is related to each other. Accordingly, theinference component 610 can employ one or more algorithms in order toinfer possible relationships between the outcome and industrialparameters involved. For example, the inference component 610 can employan algorithm that scores each potential process trend for autosuggesting by assigning a “point” for each time, an item that has beenemployed with such process trend results in a predicted outcome. Hence,trends with the highest number of points can be considered the “best”trends for auto suggestion of trends corresponding to the industrialparameters involved, for example.

Moreover, selecting the trends, and which ones are likely auto suggestedcan be accomplished by employing statistical analysis. For example,calculations on the number of standard deviations away from thestatistical mean, where item(s) more than two standard deviations away,can be employed for auto suggesting a trend based on correlationsinvolved.

In another example, the inference component 610 can employ a Bayesianclassifier style of categorization. Accordingly, the inference component610 typically computes the probability of a trend correlated toparameters of an industrial process actually occurs. The inferencecomponent 610 can employ the probabilities to suggest inferredrelationships among the various correlations. In yet a further relatedexample, the inference component 610 can score each potential trend forauto suggestion by assigning it a point for each time the tagging trendhas actually occurred. For example, the trends with the highest numberof points can be considered suitable for auto suggestion. It is to beappreciated that the inference component 610 can employ any appropriateinference algorithm for inferring relationship between the collecteddata and outcome of the industrial process.

In a related aspect, the inference component 610 can further employ anartificial intelligence (AI) component to facilitate correlating outcomeof an industrial process with historian data and/or events. As usedherein, the term “inference” refers generally to the process ofreasoning about or inferring states of the system, environment, and/oruser from a set of observations as captured via events and/or data.Inference can be employed to identify a specific context or action, orcan generate a probability distribution over states, for example. Theinference can be probabilistic—that is, the computation of a probabilitydistribution over states of interest based on a consideration of dataand events. Inference can also refer to techniques employed forcomposing higher-level events from a set of events and/or data. Suchinference results in the construction of new events or actions from aset of observed events and/or stored event data, whether or not theevents are correlated in close temporal proximity, and whether theevents and data come from one or several event and data sources.

For example, a process for correlating trends between historiandata/events can be facilitated via an automatic classifier system andprocess. A classifier is a function that maps an input attribute vector,x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to aclass, that is, f(x)=confidence(class). Such classification can employ aprobabilistic and/or statistical-based analysis (e.g., factoring intothe analysis utilities and costs) to prognose or infer an action that auser desires to be automatically performed.

A support vector machine (SVM) is an example of a classifier that can beemployed. The SVM operates by finding a hypersurface in the space ofpossible inputs, which hypersurface attempts to split the triggeringcriteria from the non-triggering events. Intuitively, this makes theclassification correct for testing data that is near, but not identicalto training data. Other directed and undirected model classificationapproaches include, e.g., naïve Bayes, Bayesian networks, decisiontrees, neural networks, fuzzy logic models, and probabilisticclassification models providing different patterns of independence canbe employed. Classification as used herein also is inclusive ofstatistical regression that is utilized to develop models of priority.

As will be readily appreciated from the subject specification, thesubject innovation can employ classifiers that are explicitly trained(e.g., via a generic training data) as well as implicitly trained (e.g.,via observing user behavior, receiving extrinsic information). Forexample, SVM's are configured via a learning or training phase within aclassifier constructor and feature selection module. Thus, theclassifier(s) can be used to automatically learn and perform a number offunctions, including but not limited to determining according to apredetermined criteria when to update or refine the previously inferredschema, tighten the criteria on the inferring algorithm based upon thekind of data being processed.

FIG. 7 illustrates a system 700 that includes a plurality of embeddedhistorian components 710 operating in an organizational data model,wherein a correlation component 719 enables discovery of relations andcorrelates among disparate pieces of data, to infer possiblerelationships between the industrial process and historian data/events.Moreover, a locator component 709 can detect embedded historians (e.g.,micro historians) that are distributed on the back plane of anassociated industrial network. In addition, the embedded historiancomponents 710 can be distributed across a network 714 to provide acollective or distributed database. The locator component 709 can bepart of applications running on a control unit 730, which can functionas a management control center for the industrial network system.

The industrial setting or organizational enterprise 700 can employ aplurality of computers or network components that communicate across thenetwork 714, to one or more industrial control components 730, such asfor example programmable logic controllers (PLCs) 711,712, 713 (1 to j,j being an integer) or other factory components. Thus, the embeddedhistorian components 710 can be operated as a singular or collectiveentity while being viewed, managed and distributed across substantiallyall or portions of the enterprise 720, control component 730 and/orlocator component 709. For example, at the control levels 730, embeddedhistorians can be embedded within a PLC rack to collect data, whereashigher levels at 720 can be employed to aggregate data from lowerlevels. Such can include higher level software components thatcommunicate across the network 714 to collect data from lower levelcontrol components. The network 714 can include public networks such asthe Internet, Intranets, and automation networks such as Control andInformation Protocol (CIP) networks including DeviceNet and ControlNet.Other networks include Ethernet, DH/DH+, Remote I/O, Fieldbus, Modbus,Profibus, wireless networks, serial protocols, and the like. Inaddition, the network devices can include various possibilities(hardware and/or software components). These include components such asswitches with virtual local area network (VLAN) capability, LANs, WANs,proxies, gateways, routers, firewalls, virtual private network (VPN)devices, servers, clients, computers, configuration tools, monitoringtools, and/or other devices.

Likewise, the industrial/enterprise 720 can include various computer ornetwork components such as servers, clients, communications modules,mobile computers, wireless components, and the like which are capable ofinteracting across the network 714. Similarly, the term PLC as usedherein can include functionality that can be shared across multiplecomponents, systems, and/or networks 714. For example, one or more PLCsof the control component 730 can communicate and cooperate with variousnetwork devices across the network 714. Such can include substantiallyany type of control, communications module, computer, I/O device,sensor, Human Machine Interface (HMI)) that communicate via the network714 which includes control, automation, and/or public networks. The PLC730 can also communicate to and control various other devices such asInput/Output modules including Analog, Digital, Programmed/IntelligentI/O modules, other programmable controllers, communications modules, andthe like.

The system 700 enables combining organizational information such as anorganizational or hierarchical data model which represents a commonmodel of a plant that can be based in the S88 or S95 model, and isdistributed among computers of the enterprise 720 and industrialcontrollers 730, for example. The model can be viewed as anOrganizational Data Model—a tree-like hierarchical and heterogeneousstructure of organizational Units. For instance, respectiveOrganizational Units can include other Organizational Units.Organizational Units can be either physical locations (e.g., Site, Area)or logical grouping node or collection (e.g., Enterprise as a collectionof Sites). The nodes in the organizational hierarchy or model can haveassociated items representing the plant's production and controlequipment, tags, backing tags (e.g., Alarm & Event and the like),programs, equipment phases, I/O devices, and other application relatedentities. These organizational units thus can form an application viewof the user's system.

A typical system 700 can assign the upper levels of the hierarchy suchas an Enterprise node and site to a computer system and the lower levelssuch as area, line, cell and machine can be contained in multipleindustrial controllers 730; each of which can include components thatare members of one or more organization units such as area or areamodel. Moreover, an organization unit can contain components from one ormore controllers. The embedded historian component 710 can be positionedat various levels of the enterprise 720 and/or control 730; and can alsofurther be integrated therein and scaled according to system datacollection requirements. Such organizational model enables embeddedhistorian components 710 to locate data of interest for collectionpurposes and to readily adapt and become integrated within the largersystem 700.

Adaptability within the system 700 can be facilitated by data havingadditional information such as metadata that identifies the purpose ofthe data. Such metadata can further be employed by the locator component709 to identify a micro-historian. For example, the locator component709 can employ a trail of metadata to identify the embedded historiansand relevant historian data for collection

Accordingly, one form of data can identify itself as a control tag thathas been marked or labeled via metadata to indicate its significance fordata collection purposes. Another type of label or metadata can indicatesecurity information that is being distributed throughout the system700. Furthermore, other type of data can indicate that an alarmcondition or an event has occurred within the system and thus, arespective embedded historian component should capture such alarm orevent. In general, the organizational model enables embedded historiancomponents 710 to receive functionality or data context from the system700 and to expose its respective functionality to the system via themodel. For example, context allows embedded historian components to suchauto configuration routines where one or more components of the embeddedhistorian architecture can be automatically discovered and configuredonto a respective system. Hence, the embedded historian components 710and the locator component 709 can be automatically integrated within thesystem 700, to further facilitate scaling of the system as dataconditions change.

In a related aspect, such scaling can include the ability of one or morecomponents of an organization to collaborate, and provide an overallscheme for historical data collection. Such can include having lowerlevel PLCs or factory components collecting data and sharing this datawith higher levels of the organization. If one or more of the levelsbecome overloaded with the data collection process, historianfunctionality can be shifted between levels (upwards or downwards) tomore effectively employ system-wide resources in an efficient manner.For instance, communications between levels can allow sharing of datacollection responsibilities between one or more levels of the enterprisefrom the very lowest levels through the higher levels of theorganizational hierarchy.

For example, the lowest level entity can have sufficient memory for datacollection of desired embedded historian or archived information. Ifsuch memory resources are consumed, messaging capabilities throughoutthe hierarchy can subsequently take over to distribute storageresponsibilities from one layer to another via suitable network messages(wireless or wired) that communicate data from one level to another. Itis to be appreciated that tiers of an organization can collaborate inmany combinations. Thus, a high level tier could collaborate with a lowlevel tier or collaboration can take place between multiple tiers ifdesired such as between higher levels, intermediate levels, and lowerlevels of an organization.

The locator component 709 can identify embedded historians (e.g.,micro-historians), and notify them to collect various data types. Thelocator component 709 can subsequently locate embedded historians thathave collected or have access to a collection of such data type. Next,the data can be acquired, supplied and reported to the user via aplurality of interfaces. Such interfaces can be supplied to manipulatethe embedded historian components 710 and organizational data model;such as a Graphical User Interface (GUI) to interact with the model orother components of the hierarchy; e.g., as any type of application thatsends, retrieves, processes, and/or manipulates factory or enterprisedata, receives, displays, formats, and/or communicates data, and/orfacilitates operation of the enterprise 720 and/or PLCs 730. Forexample, such interfaces can also be associated with an engine, server,client, editor tool or web browser although other type applications canbe utilized.

FIG. 8 illustrates a correlation component 825 that is operativelyconnected to embedded historian network/embedded historians 800 inaccordance with an aspect of the subject innovation. The industrialsetting 805 can employ a hierarchical data model with various level;e.g., enterprise level, site level (factory represented within a datapacket), area level (an area within the factory associated with thedata); line level (a line associated with particular data), a work-celllevel (that indicates a work-cell associated with the data) and thelike. For example by employing a nested, hierarchical data model,embedded historian components 800 can readily become aware of dataassociated therewith. Furthermore, such hierarchy can further becustomized by users to obtain increased granularity within thehierarchy. The common plant model can enable the embedded historiancomponent 800 to determine data contexts in an automated manner. Thecommon data model 810 allows data to be marked or labeled via metadatafor example to both expose embedded historian functionality to a systemand/or to allow the embedded historian component 800 to be automaticallyintegrated within the system according to data that is exposed to theembedded historian component. For example, one such labeling can pertainto security, and typically can affect substantially all components inthe system associated with the common model 810.

The correlation component 825 can be associated with a directory anddiscovery service. Such an arrangement enables the embedded historiancomponent 800 to locate other embedded historian components in thesystem and to receive/expose historian data to other system components.This can include a network directory that determines physical addressesfrom logical names and vice versa, for example. Moreover, the publishand subscribe component 830 can provide subscription functionality tothe embedded historian component 800, wherein data collection efficiencyof the system can be enhanced. For example, the publish and subscribecomponent 830 of the system 805 allows data to be published or generatedwhen a change in the data has been detected. Thus, the embeddedhistorian component 800 can subscribe to such change events and thusonly record data when a change has occurred which reduces the amount ofdata to be stored. Additionally, a polling/publication arrangement canalso be employed wherein the embedded historians (e.g.,micro-historians) identify themselves to the correlation component 825upon occurrence of a predetermined event, and/or periodically.

FIG. 9 illustrates an exemplary industrial automation network thatemploys an embedded historian component 933, to enable high speed datacollection (e.g., real time) from the industrial setting 900, forcorrelation with outcome of an industrial process. As such, thecorrelation component 961 can correlate among disparate pieces of datato infer possible relationships between the industrial process andhistorian data/events to improve industrial operations. The system 900can further include a database 910, a human machine interface (HMI) 920and a programmable logic controller (PLC) 930, and a directory interface940. The directory interface 940 can further associate with anArtificial Intelligence (AI) component 950 to facilitate efficientidentification of desired data within a particular network/application,and for correlation of the events/data with the industrial outcome.

The directory interface 940 can be employed to provide data from anappropriate location such as the data source 960, a server 970 and/or aproxy server 980. Accordingly, the directory interface 940 can point toa source of data based upon role and requirements (needs) of a requester(e.g., database 910, HMI 920, PLC 930, and the like.) The database 910can be any number of various types such as a relational, network,flat-file or hierarchical systems. Typically, such databases can beemployed in connection with various enterprise resource planning (ERP)applications that can service any number of various business relatedprocesses within a company. For example, ERP applications can be relatedto human resources, budgeting, forecasting, purchasing and the like. Inthis regard, particular ERP applications may require data that hascertain desired attributes associated therewith. Thus, in accordancewith an aspect of the subject invention, the directory interface 940 canprovide data to the database 910 from the server 970, which providesdata with the attributes desired by the database 910.

As illustrated in FIG. 9, the embedded historian 933 can leveragedirectory interface 940 and other software services/re-locatableinformation services to locate other embedded historian components andtheir configurations. In addition, the correlation component 961 canfurther detect embedded historians 933 that are distributed on the backplane of an industrial network.

Accordingly, the correlation component 961 can employ models tocorrelate various data (e.g., events, command, event, alarm, scenarios,transactions, messages, and the like), with outcome of the industrialprocess.

Moreover, the HMI 920 can employ the directory interface 940 to point todata located within the system 900. The HMI 920 can be employed tographically display various aspects of a process, system, factory, etc.to provide a simplistic and/or user-friendly view of the system.Accordingly, various data points within a system can be displayed asgraphical (e.g., bitmaps, jpegs, vector based graphics, clip art and thelike) representations with desired color schemes, animation, and layout.

The HMI 920 can request data to have particular visualization attributesassociated with data in order to easily display such data thereto. Forexample, the HMI 920 can query the directory interface 940 for aparticular data point that has associated visualization attributes. Thedirectory interface 940 can determine the proxy server 980 contains theattributed data point with the desired visualization attributes. Forinstance, the attributed data point can have a particular graphic thatis either referenced or sent along with the data such that this graphicappears within the HMI environment instead of or along with the datavalue.

As explained earlier, the PLC 930 can be any number of models such asAllen Bradley PLC5, SLC-500, MicoLogix, and the like. The PLC 930 isgenerally defined as a specialized device employed to providehigh-speed, low-level control of a process and/or system. The PLC 930can be programmed using ladder logic or some form of structuredlanguage. Typically, the PLC 930 can utilize data directly from a datasource (e.g., data source 960) that can be a sensor, encoder,measurement sensor, switch, valve and the like. The data source 960 canprovide data to a register in a PLC and such data can be stored in thePLC if desired. Additionally, data can be updated (e.g., based on aclock cycle) and/or output to other devices for further processing.

FIG. 10 illustrates an exemplary multi-tiered and distributed historiansystem 1000, which can employ a correlation component in accordance withan aspect of the subject innovation. The exemplary system 1000illustrates three tiered historian level, wherein the highest datacollection tier is illustrated and can be referred to as the enterprisetier 1010. This tier aggregates data collected from lower level tierssuch as from a plant tier 1020 and a micro or embedded tier 1030. Asillustrated, the tiers 1010 and 1020 can include archival or permanentstorage capabilities. In the system 1000, data can be collected from twoplants at the tier 1020, and from a plurality of historian components attier 1030. It is to be appreciated that such an arrangement is exemplaryin nature, and other arrangements are well within the realm of thesubject innovation.

Typically, the system 1000 can be viewed as a Distributed Historian thatspans machines, plants, and enterprises. At level 1030, the historiancollects data at the rack level and is coupled to Common Plant DataStructure described above. Such can include collecting process &discrete data, alarms & events in a single archive if desired. Otheraspects can include auto-discovery of data and context from controllersin local chassis including store/forward data capabilities from localbuffers. Data can be collected without polling, having a lowcommunications bandwidth. The plant level 1020 aggregates data fromMicro or rack-embedded Historians and/or other data sources (e.g., LiveData source). Such can include plant-level querying, analytics,reporting while efficiently storing, retrieving, and managing largeamounts of data. This level can also auto-discover data and data modelcontext from Micro Historians located at level 1030. Other features ofthe system 1000 can include analysis components, logical units,components for interaction with report elements, embeddable presentationcomponents, replication of configuration, storage, archiving, datacompression, summarization/filtering, security, and scalability.

FIG. 11 illustrates historian services 1100 that include historian dataservices 1110 and presentation and reporting services 1120. HistorianData Services 1110 (HDS) can supply generic, customizable services forcollecting and storing data with plant model-defined context. This caninclude configuration of data to be collected e.g., tags, data context,alarms, events, diagnostics, SOE data and configuration of data to beforwarded to a higher level. Collection of data can be from disparatesources including storage of data, retrieval of data, and management ofdata. Management of data collected by/residing in other data stores(e.g., higher-level business systems, 3rd party products) can beprocessed by the respective applications. The presentation and reportingservices 1120 (PRS) can supply generic, customizable services forcollating and presenting data in a common plant model-defined context.This can include access to stored data, analysis/calculators and querymechanisms, and embeddable, interactive presentation components (e.g.,text, charts, SPC). The service 1110 can generate reports with variousmeans of presentation/distribution (e.g., web, email) having exportcapabilities to standard formats (e.g., XML, Excel).

FIG. 12 illustrates an exemplary environment that can employ correlationcomponent that can identify trends in accordance with an aspect of thesubject innovation. As illustrated, each functional module 1214, isattached to the backplane 1216 by means of a separable electricalconnector 1230 that permits the removal of the module 1214 from thebackplane 1216 so that it may be replaced or repaired without disturbingthe other modules 1214. The backplane 1216 provides the module 1214 withboth power and a communication channel to the other modules 1214. Localcommunication with the other modules 1214 through the backplane 1216 isaccomplished by means of a backplane interface 1232 which electricallyconnects the backplane 1216 through connector 1230. The backplaneinterface 1232 monitors messages on the backplane 1216 to identify thosemessages intended for the particular module 1214, based on a messageaddress being part of the message and indicating the messagedestination. Messages received by the backplane interface 1232 areconveyed to an internal bus 1234 in the module 1214.

The internal bus 1234 joins the backplane interface 1232 with a memory1236, a microprocessor 1228, front panel circuitry 1238, I/O interfacecircuitry 1239 and communication network interface circuitry 1241. Themicroprocessor 1228 can be a general purpose microprocessor providingfor the sequential execution of instructions included within the memory1236 and the reading and writing of data to and from the memory 1236 andthe other devices associated with the internal bus 1234. Themicroprocessor 1228 includes an internal clock circuit (not shown)providing the timing of the microprocessor 1228 but may also communicatewith an external clock 1243 of improved precision. This clock 1243 maybe a crystal controlled oscillator or other time standard including aradio link to an external time standard. The precision of the clock 1243may be recorded in the memory 1236 as a quality factor. The panelcircuitry 1238 includes status indication lights such as are well knownin the art and manually operable switches such as for locking the module1214 in the off state.

The memory 1236 can comprise control programs or routines executed bythe microprocessor 1228 to provide control functions, as well asvariables and data necessary for the execution of those programs orroutines. For I/O modules, the memory 1236 may also include an I/O tableholding the current state of inputs and outputs received from andtransmitted to the industrial controller 1210 via the I/O modules 1220.The module 1214 can be adapted to perform the various methodologies ofthe innovation, via hardware configuration techniques and/or by softwareprogramming techniques.

It is noted that as used in this application, terms such as “component,”“hierarchy,” “model,” and the like are intended to refer toelectromechanical components, and/or a computer-related entity, eitherhardware, a combination of hardware and software, software, or softwarein execution as applied to an automation system for industrial control.For example, a component may be, but is not limited to being, a processrunning on a processor, a processor, an object, an executable, a threadof execution, a program and a computer. By way of illustration, both anapplication running on a server and the server can be components. One ormore components may reside within a process and/or thread of executionand a component may be localized on one computer and/or distributedbetween two or more computers, industrial controllers, and/or modulescommunicating therewith.

What has been described above includes various exemplary aspects. It is,of course, not possible to describe every conceivable combination ofcomponents or methodologies for purposes of describing these aspects,but one of ordinary skill in the art may recognize that many furthercombinations and permutations are possible. In particular regard to thevarious functions performed by the above described components(assemblies, devices, circuits, systems, etc.), the terms (including areference to a “means”) used to describe such components are intended tocorrespond, unless otherwise indicated, to any component which performsthe specified function of the described component (e.g., that isfunctionally equivalent), even though not structurally equivalent to thedisclosed structure, which performs the function in the hereinillustrated exemplary aspects of the innovation. In this regard, it willalso be recognized that the innovation includes a system as well as acomputer-readable medium having computer-executable instructions forperforming the acts and/or events of the various methods of theinnovation. Furthermore, to the extent that the term “includes” is usedin either the detailed description or the claims, such term is intendedto be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

1. An industrial automation system, comprising: a plurality of embeddedhistorians that collect data associated with the industrial automationsystem; and a correlation component that infers relationships betweenevents and operation of the industrial automation system based on thedata.
 2. The industrial automation system of claim 1, the correlationcomponent further comprising an explicit correlation component thatemploys predetermined models.
 3. The industrial automation system ofclaim 1, the correlation component further comprising an implicitcorrelation component that infers relationships in real time.
 4. Theindustrial automation system of claim 1, the correlation componentfurther comprising heuristic models.
 5. The industrial automation systemof claim 1 further comprising an organizational hierarchy data modelwith nodes that represent units associated with the industrialautomation system.
 6. The industrial automation system of claim 1further comprising an artificial intelligence component that facilitatestrend verification between the data and an outcome of the industrialprocess.
 7. The industrial automation system of claim 1 furthercomprising a Human Machine Interface (HMI) to graphically display a viewof the industrial automation system.
 8. The industrial automation systemof claim 1 further comprising a graphical user interface (GUI) thatforms an application view of a historian data collection system.
 9. Theindustrial system of claim 5, the common organizational hierarchy datamodel facilitates data determined for collection.
 10. The industrialsystem of claim 5, the embedded historians associated with at least oneof a controller, a module in a chassis, a server, a sensor, and afactory component.
 11. The industrial system of claim 4, theorganizational hierarchy data model employs metadata for identificationof embedded historians.
 12. The industrial system of claim 10, furthercomprising a publish and subscribe component that identifies embeddedhistorians to a locator component.
 13. A method of inferringrelationships in an industrial automation system comprising: collectingdata via a plurality of embedded historians; and inferring a trend foroperation of the industrial automation system based on the data.
 14. Themethod of claim 13 further comprising employing heuristics models toinfer the trend.
 15. The method of claim 13 further comprisingidentifying a data type for collection by the embedded historian. 16.The method of claim 15 further comprising employing metadata tofacilitate a trail to the embedded historian.
 17. The method of claim 15further comprising defining a common organizational data model for theindustrial plant.
 18. The method of claim 17 further comprisingemploying a directory to track source of data.
 19. The method of claim17 further comprising collecting historian data across various levels ofthe industrial plant.
 20. An industrial controller system comprising:collection means for collecting data related to the industrialcontroller system; means for locating the collection means; and meansfor inferring trends of operation associated with the industrialcontroller system based on the data.